#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

#-*- coding: utf-8 -*-

import paddle.fluid as fluid
import parl
from parl import layers


class Model(parl.Model):
    def __init__(self, act_dim):
        self.actor_model = ActorModel(act_dim)
        self.critic_model = CriticModel()

    def policy(self, obs):
        return self.actor_model.policy(obs)

    def value(self, obs):
        return self.critic_model.value(obs)

    def get_actor_params(self):
        return self.actor_model.parameters()


class ActorModel(parl.Model):
    def __init__(self, act_dim):
        hid_size = 100

        # self.fc1 = layers.fc(size=hid_size, act='relu')
        # self.fc2 = layers.fc(size=act_dim, act='tanh')

        ch1 = 256
        ch2 = 4096
        self.c1 = layers.conv2d(num_filters=ch1, filter_size=(2, 1), act="relu", bias_attr=True)
        self.c2 = layers.conv2d(num_filters=ch1, filter_size=(1, 2), act="relu", bias_attr=True)
        self.c11 = layers.conv2d(num_filters=ch2, filter_size=(2, 1), act="relu", bias_attr=True)
        self.c12 = layers.conv2d(num_filters=ch2, filter_size=(1, 2), act="relu", bias_attr=True)
        self.c21 = layers.conv2d(num_filters=ch2, filter_size=(2, 1), act="relu", bias_attr=True)
        self.c22 = layers.conv2d(num_filters=ch2, filter_size=(1, 2), act="relu", bias_attr=True)
        self.fc1 = layers.fc(size=256, act='relu')
        self.fc2 = layers.fc(size=act_dim, act='softmax')

    def policy(self, obs):
        # hid = self.fc1(obs)
        # means = self.fc2(hid)
        r1 = layers.flatten(self.c1(obs), axis=1)
        r2 = layers.flatten(self.c2(obs), axis=1)
        r11 = layers.flatten(self.c11(self.c1(obs)), axis=1)
        r12 = layers.flatten(self.c12(self.c1(obs)), axis=1)
        r21 = layers.flatten(self.c21(self.c2(obs)), axis=1)
        r22 = layers.flatten(self.c22(self.c2(obs)), axis=1)
        hidden = layers.concat(input=[r1, r2, r11, r12, r21, r22], axis=1)
        # 输出动作选择概率
        out = self.fc2(self.fc1(hidden))
        return out


class CriticModel(parl.Model):
    def __init__(self):
        # hid_size = 100
        #
        # self.fc1 = layers.fc(size=hid_size, act='relu')
        # self.fc2 = layers.fc(size=1, act=None)

        ch1 = 256
        ch2 = 4096
        self.c1 = layers.conv2d(num_filters=ch1, filter_size=(2, 1), act="relu", bias_attr=True)
        self.c2 = layers.conv2d(num_filters=ch1, filter_size=(1, 2), act="relu", bias_attr=True)
        self.c11 = layers.conv2d(num_filters=ch2, filter_size=(2, 1), act="relu", bias_attr=True)
        self.c12 = layers.conv2d(num_filters=ch2, filter_size=(1, 2), act="relu", bias_attr=True)
        self.c21 = layers.conv2d(num_filters=ch2, filter_size=(2, 1), act="relu", bias_attr=True)
        self.c22 = layers.conv2d(num_filters=ch2, filter_size=(1, 2), act="relu", bias_attr=True)
        self.fc1 = layers.fc(size=256, act='relu')
        self.fc2 = layers.fc(size=4, act='relu')

    def value(self, obs):
        # concat = layers.concat([obs, act], axis=1)
        # hid = self.fc1(concat)
        # Q = self.fc2(hid)
        # Q = layers.squeeze(Q, axes=[1])
        # concat = layers.concat([obs, act], axis=1)
        r1 = layers.flatten(self.c1(obs), axis=1)
        r2 = layers.flatten(self.c2(obs), axis=1)
        r11 = layers.flatten(self.c11(self.c1(obs)), axis=1)
        r12 = layers.flatten(self.c12(self.c1(obs)), axis=1)
        r21 = layers.flatten(self.c21(self.c2(obs)), axis=1)
        r22 = layers.flatten(self.c22(self.c2(obs)), axis=1)
        # print(r22)
        # print(r22.shape)
        # print(act)
        # print(act.shape)
        # hidden = layers.concat(input=[r1, r2, r11, r12, r21, r22, act], axis=1)#??
        hidden = layers.concat(input=[r1, r2, r11, r12, r21, r22], axis=1)  # ??
        Q = self.fc2(self.fc1(hidden))
        return Q
